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Dear Jenine,

You have your nodes in two groups.  It seems you want to partition your 
collection of links into two networks - those linking to pairs in the 
same classes and those to pairs in different classes.  I think of this 
as two separate calculations or transformations.    Assume that the 
original network is called is_linked_to, then we want to calculate two 
new networks: is_similar_to and is_dissimilar_to.   All three networks 
may have the same nodes and is_similar_to links a->a and b->b whereas 
is_dissimilar_to links a->b and b->a.

This can be done in VisuaLyzer of the SocioMetrica Suite at 
http://mdlogix.com/visualyzer.htm in a somewhat straightforward manner 
if your data can be exported from Pajek to UCINET format and then 
imported into VisuaLyzer.   If your nodes have two attributes, a name 
and a class, then your partition is based on this second attribute.   In 
the reasoning language this second attribute is selected with the 
relation, node^2-2.  This effectively says 'Of the two attributes of the 
node, give me the second.'   (Similarly, node^2-1 returns the name.)    
Alternatively, converse(node^2-2), constructs the second attribute.  The 
composition of the two (the relative product) is  
(node^2-2:converse(node^2-2)); it is smaller than the identity relation.

Therefore we can intersect this with the original to define 
is_similar_to as the product 
is_linked_to*(node^2-2:converse(node^2-2)).  Procedurally, this is 
computed in VisuaLyzer by first typing this into the box called 
'Relation Expression' brought to the top with the ^R command.   Be sure 
to rename the resulting expression as is_similar_to and check the box 
labelled 'Extend the existing collection with this new network'.

Also, we may define is_dissimilar_to as 
is_linked_to*(node^2-2:di:converse(node^2-2)) since the apartness 
relation is di.   All this results in three networks linking the same 
set of nodes.   Some understanding may come by observing that 
is_linked_to is the sum (union) of the other two collections of links.  

I hope this helps.  This may require some experimentation, a 
fundamentally different way of thinking, and more discussion.

  paul
 
jenine harris wrote:

> *****  To join INSNA, visit http://www.insna.org  *****
>
> Hi networkers,
>
> I have a large-ish directed network (1877 nodes) in Pajek with a 
> partition
> that classifies each node into one of two categories. For the most 
> part the
> nodes in each category only are connected to other nodes in the same
> category (Category A --> Category A). However, there is a proportion of
> nodes that are linked to nodes in the other category (Category A -->
> Category B). Is there a way in Pajek to extract/identify these nodes that
> are involved in cross-category connections? I've gone through the book 
> and
> have moderate experience with the software and still just can't come 
> up with
> anything. My final goal here is to make a new partition with Category A,
> Category B, and Category AB which would be those nodes involved in cross
> category connections.
>
> I also have access to UCINET, but am much more comfortable with Pajek.
>
> Thanks in advance for your help.
>
> See you in Corfu!
>
> jenine
>

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